1 / 30

Chapter 5: Mining Frequent Patterns, Association and Correlations

Chapter 5: Mining Frequent Patterns, Association and Correlations. What Is Frequent Pattern Analysis?. Frequent pattern : a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set

zohar
Download Presentation

Chapter 5: Mining Frequent Patterns, Association and Correlations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 5: Mining Frequent Patterns, Association and Correlations

  2. What Is Frequent Pattern Analysis? • Frequent pattern: a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set • First proposed by Agrawal, Imielinski, and Swami [AIS93] in the context of frequent itemsets and association rule mining • Motivation: Finding inherent regularities in data • What products were often purchased together?— Beer and diapers?! • What are the subsequent purchases after buying a PC? • What kinds of DNA are sensitive to this new drug? • Can we automatically classify web documents? • Applications • Basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis.

  3. Why Is Freq. Pattern Mining Important? • Discloses an intrinsic and important property of data sets • Forms the foundation for many essential data mining tasks • Association, correlation, and causality analysis • Sequential, structural (e.g., sub-graph) patterns • Pattern analysis in spatiotemporal, multimedia, time-series, and stream data • Classification: associative classification • Cluster analysis: frequent pattern-based clustering • Data warehousing: iceberg cube and cube-gradient • Semantic data compression: fascicles • Broad applications

  4. Customer buys both Customer buys diaper Customer buys beer Basic Concepts: Frequent Patterns and Association Rules • Itemset X = {x1, …, xk} • Find all the rules X  Ywith minimum support and confidence • support, s, probability that a transaction contains X  Y • confidence, c,conditional probability that a transaction having X also contains Y • Let supmin = 50%, confmin = 50% • Freq. Pat.: {A:3, B:3, D:4, E:3, AD:3} • Association rules: • A  D (60%, 100%) • D  A (60%, 75%)

  5. Association Rule • What is an association rule? • An implication expression of the form X  Y, where X and Y are itemsets and XY= • Example: {Milk, Diaper}  {Beer}

  6. 2. What is association rule mining? • To find all the strong association rules • An association rule r is strong if • Support(r) ≥ min_sup • Confidence(r) ≥ min_conf • Rule Evaluation Metrics • Support (s): Fraction of transactions that contain both X and Y • Confidence (c): Measures how often items in Y appear in transactions that contain X

  7. Example of Support and Confidence To calculate the support and confidence of rule {Milk, Diaper}  {Beer} • # of transactions: 5 • # of transactions containing {Milk, Diaper, Beer}: 2 • Support: 2/5=0.4 • # of transactions containing {Milk, Diaper}: 3 • Confidence: 2/3=0.67

  8. Definition: Frequent Itemset • Itemset • A collection of one or more items • Example: {Bread, Milk, Diaper} • k-itemset • An itemset that contains k items • Support count () • # transactions containing an itemset • E.g. ({Bread, Milk, Diaper}) = 2 • Support (s) • Fraction of transactions containing an itemset • E.g. s({Bread, Milk, Diaper}) = 2/5 • Frequent Itemset • An itemset whose support is greater than or equal to a min_sup threshold

  9. Association Rule Mining Task • An association rule r is strong if • Support(r) ≥ min_sup • Confidence(r) ≥ min_conf • Given a transactions database D, the goal of association rule mining is to find all strong rules • Two-step approach: 1. Frequent Itemset Identification • Find all itemsets whose support  min_sup 2. Rule Generation • From each frequent itemset, generate all confident rules whose confidence  min_conf

  10. Rule Generation Suppose min_sup=0.3, min_conf=0.6, Support({Beer, Diaper, Milk})=0.4 All candidate rules: {Beer}  {Diaper, Milk} (s=0.4, c=0.67) {Diaper}  {Beer, Milk} (s=0.4, c=0.5) {Milk}  {Beer, Diaper} (s=0.4, c=0.5) {Beer, Diaper}  {Milk} (s=0.4, c=0.67) {Beer, Milk}  {Diaper} (s=0.4, c=0.67) {Diaper, Milk}  {Beer} (s=0.4, c=0.67) All non-empty real subsets {Beer} , {Diaper} , {Milk}, {Beer, Diaper}, {Beer, Milk} , {Diaper, Milk} Strong rules: {Beer}  {Diaper, Milk} (s=0.4, c=0.67) {Beer, Diaper}  {Milk} (s=0.4, c=0.67) {Beer, Milk}  {Diaper} (s=0.4, c=0.67) {Diaper, Milk}  {Beer} (s=0.4, c=0.67)

  11. Frequent Itemset Indentification: the Itemset Lattice Level 0 Level 1 Level 2 Level 3 Level 4 Given I items, there are 2I-1 candidate itemsets! Level 5

  12. Frequent Itemset Identification: Brute-Force Approach • Brute-force approach: • Set up a counter for each itemset in the lattice • Scan the database once, for each transaction T, • check for each itemset S whether T S • if yes, increase the counter of S by 1 • Output the itemsets with a counter ≥ (min_sup*N) • Complexity ~ O(NMw) Expensive since M = 2I-1 !!!

  13. BRUTE FORCE EXAMPLE List all possible combinations in an array. a 6 cd 3 abce 0 b 6 acd 1 de 3 ab 3 bcd 1 ade 1 • For each record: • Find all combinations. • For each combination index into array and increment support by 1. • Then generate rules c 6 abcd 0 bde 1 ac 3 e 6 abde 0 bc 3 ae 3 cde 1 abc 1 be 3 acde 0 d 6 abe 1 bcde 0 ad 6 ce 3 abcde 0 bd 3 ace 1 abd 1 bce 1

  14. Support threshold = 5% Frequents Sets (F): ab(3) ac(3) bc(3) ad(3) bd(3) cd(3) ae(3) be(3) ce(3) de(3) a 6 cd 3 abce 0 b 6 acd 1 de 3 ab 3 bcd 1 ade 1 c 6 abcd 0 bde 1 Rules: ab conf=3/6=50% ba conf=3/6=50% Etc. ac 3 e 6 abde 0 bc 3 ae 3 cde 1 abc 1 be 3 acde 0 d 6 abe 1 bcde 0 ad 6 ce 3 abcde 0 bd 3 ace 1 abd 1 bce 1

  15. Advantages: • Very efficient for data sets with small numbers of attributes (<20). • Disadvantages: • Given 20 attributes, number of combinations is 220-1 = 1048576. Therefore array storage requirements will be 4.2MB. • Given a data sets with (say) 100 attributes it is likely that many combinations will not be present in the data set --- therefore store only those combinations present in the dataset!

  16. How to Get an Efficient Method? • The complexity of a brute-force method is O(MNw) • M=2I-1, I is the number of items • How to get an efficient method? • Reduce the number of candidate itemsets • Check the supports of candidate itemsets efficiently

  17. Anti-Monotone Property • Any subset of a frequent itemset must be also frequent— an anti-monotone property • Any transaction containing {beer, diaper, milk} also contains {beer, diaper} • {beer, diaper, milk} is frequent  {beer, diaper} must also be frequent • In other words, any superset of an infrequent itemset must also be infrequent • No superset of any infrequent itemset should be generated or tested • Many item combinations can be pruned!

  18. Illustrating Apriori Principle Level 0 Level 1 Found to be Infrequent Pruned Supersets

  19. An Example Min. support 50% Min. confidence 50% For rule AC: support = support({AλC}) = 50% confidence = support({AλC})/support({A}) = 66.6% The Apriori principle: Any subset of a frequent itemset must be frequent

  20. Mining Frequent Itemsets: the Key Step • Find the frequent itemsets: the sets of items that have minimum support • A subset of a frequent itemset must also be a frequent itemset • i.e., if {AB} isa frequent itemset, both {A} and {B} should be frequent itemsets • Iteratively find frequent itemsets with cardinality from 1 to k (k-itemset) • Use the frequent itemsets to generate association rules.

  21. Apriori: A Candidate Generation-and-Test Approach • Apriori pruning principle: If there is any itemset which is infrequent, its superset should not be generated/tested! (Agrawal & Srikant @VLDB’94, Mannila, et al. @ KDD’ 94) • Method: • Initially, scan DB once to get frequent 1-itemset • Generate length (k+1) candidate itemsets from length k frequent itemsets • Test the candidates against DB • Terminate when no frequent or candidate set can be generated

  22. Intro of Apriori Algorithm • Basic idea of Apriori • Using anti-monotone property to reduce candidate itemsets • Any subset of a frequent itemset must be also frequent • In other words, any superset of an infrequent itemset must also be infrequent • Basic operations of Apriori • Candidate generation • Candidate counting • How to generate the candidate itemsets? • Self-joining • Pruning infrequent candidates

  23. L1 C1 Scan D C2 C2 L2 Scan D L3 C3 Scan D The Apriori Algorithm — Example Database D

  24. Data base D 1-candidates Freq 1-itemsets 2-candidates TID Items Itemset Sup Itemset Sup Itemset 10 a, c, d a 2 a 2 ab Scan D 20 b, c, e b 3 b 3 ac 30 a, b, c, e c 3 c 3 ae 40 b, e d 1 e 3 bc Min_sup=0.5 e 3 be ce Counting 3-candidates Freq 2-itemsets Scan D Itemset Sup Itemset Itemset Sup ab 1 bce ac 2 Scan D ac 2 bc 2 Freq 3-itemsets ae 1 be 3 bc 2 ce 2 Itemset Sup be 3 bce 2 ce 2 Apriori-based Mining

  25. The Apriori Algorithm • Ck: Candidate itemset of size k • Lk : frequent itemset of size k • L1 = {frequent items}; • for (k = 1; Lk !=; k++) do • Candidate Generation: Ck+1 = candidates generated from Lk; • Candidate Counting: for each transaction t in database do increment the count of all candidates in Ck+1 that are contained in t • Lk+1 = candidates in Ck+1 with min_sup • return k Lk;

  26. n/a abcd n/a n/a n/a Candidate-generation: Self-joining • Given Lk, how to generate Ck+1? Step 1: self-joining Lk INSERT INTO Ck+1 SELECT p.item1, p.item2, …, p.itemk, q.itemk FROM Lkp, Lkq WHERE p.item1=q.item1, …, p.itemk-1=q.itemk-1, p.itemk < q.itemk • Example L3={abc, abd, acd, ace, bcd} Self-joining: L3*L3 • abcdabc * abd • acdeacd * ace C4={abcd, acde} abc abc abd abd acd acd ace ace bcd bcd

  27. Candidate Generation: Pruning • Can we further reduce the candidates in Ck+1? For each itemset c in Ck+1 do For each k-subsets s of c do If (s is not in Lk) Then delete c from Ck+1 End For End For • Example L3={abc, abd, acd, ace, bcd}, C4={abcd, acde} acde cannot be frequent since ade (and also cde) is not in L3, so acde can be pruned from C4.

  28. How to Count Supports of Candidates? • Why counting supports of candidates a problem? • The total number of candidates can be very huge • One transaction may contain many candidates • Method • Candidate itemsets are stored in a hash-tree • Leaf node of hash-tree contains a list of itemsets and counts • Interior node contains a hash table • Subset function: finds all the candidates contained in a transaction

  29. Challenges of Apriori Algorithm • Challenges • Multiple scans of transaction database • Huge number of candidates • Tedious workload of support counting for candidates • Improving Apriori: the general ideas • Reduce the number of transaction database scans • Shrink the number of candidates • Facilitate support counting of candidates • Improving Apriori: the general ideas • Reduce the number of transaction database scans • DIC: Start count k-itemset as early as possible • S. Brin R. Motwani, J. Ullman, and S. Tsur, SIGMOD’97. • Shrink the number of candidates • DHP: A k-itemset whose corresponding hashing bucket count is below the threshold cannot be frequent • J. Park, M. Chen, and P. Yu, SIGMOD’95 • Facilitate support counting of candidates

  30. Performance Bottlenecks • The core of the Apriori algorithm: • Use frequent (k – 1)-itemsets to generate candidate frequent k-itemsets • Use database scan and pattern matching to collect counts for the candidate itemsets • The bottleneck of Apriori: candidate generation • Huge candidate sets: • 104 frequent 1-itemset will generate 107 candidate 2-itemsets • To discover a frequent pattern of size 100, e.g., {a1, a2, …, a100}, one needs to generate 2100  1030 candidates. • Multiple scans of database: • Needs (n +1 ) scans, n is the length of the longest pattern

More Related